Local Pros by Ron Ventures

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full behavioral burden. It earns credit for disclosing data provenance ('vetted, owner-operated') and the exact payload returned (name, phone, trade, service area, services, hours), which is helpful since there is no output schema. However, it says nothing about auth needs, result counts/pagination, ranking behavior, or how stale the listings might be.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, both load-bearing: the first defines the resource, scope and coverage; the second defines the return payload and the user's next action. Nothing is redundant or buried.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description correctly compensates by listing the returned fields, and it defines the geographic boundary of the dataset. It is nearly complete for a simple lookup tool, missing only operational details like result limits or ranking/pagination behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents city, trade (including the enum and 'any' default) and the optional problem parameter. The description adds only a loose geographic framing ('near a US city') and does not expand on parameter formats or behavior, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (find) and resource (vetted, owner-operated local service businesses) and even enumerates trades and the geographic footprint. It is very clear what the tool does, but it never explicitly names or differentiates itself from the sibling request_quote — the closing phrase 'so the user can call or request a quote' only hints at the downstream alternative.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The described coverage ('Oklahoma, Kansas, Nebraska, Iowa and Missouri metros') effectively tells the agent when the tool applies and implicitly when it does not (outside those states). There is no explicit statement of exclusions or a direct comparison to request_quote, so it falls short of full routing guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does well: it discloses the side effect (the business is emailed with name, contact and problem), that the business replies directly to the customer, and that invoking it shares the user's personal data — a privacy disclosure an agent needs. It stops short of stating auth requirements, failure modes, or idempotency, but the material behavior is covered.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three short sentences, zero filler: purpose first, mechanism second, safety precondition last. Each sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a side-effecting tool with no annotations and no output schema, the description covers the essentials: what is sent, to whom, and the permission requirement. It leaves unspecified what the tool returns on success and how errors surface, which is a minor gap but the agent has enough to call it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 75% and the four fields are largely self-describing, so baseline is 3. The description names three of the four inputs (name, contact, problem) in prose but adds no format, constraint, or validation detail beyond what the schema and its field descriptions already provide.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('send a quote request') and pins the target to businesses 'returned by find_local_pros', which uniquely distinguishes it from its only sibling. An agent knows exactly what this does and where its input comes from without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives a clear prerequisite chain: call find_local_pros first, then request a quote, and obtain user permission before invoking. It does not state what to do if the user declines or whether repeat requests are allowed, but the core when-to-use context is explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.